{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Give Me Some Credit\n",
    "## 导入数据\n",
    "题目已经给定训练集和测试集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#读取训练集和测试集\n",
    "train = pd.read_csv('data\\cs-training.csv')\n",
    "test = pd.read_csv('data\\cs-test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "简单地查看数据集的信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150000 entries, 0 to 149999\n",
      "Data columns (total 12 columns):\n",
      "Unnamed: 0                              150000 non-null int64\n",
      "SeriousDlqin2yrs                        150000 non-null int64\n",
      "RevolvingUtilizationOfUnsecuredLines    150000 non-null float64\n",
      "age                                     150000 non-null int64\n",
      "NumberOfTime30-59DaysPastDueNotWorse    150000 non-null int64\n",
      "DebtRatio                               150000 non-null float64\n",
      "MonthlyIncome                           120269 non-null float64\n",
      "NumberOfOpenCreditLinesAndLoans         150000 non-null int64\n",
      "NumberOfTimes90DaysLate                 150000 non-null int64\n",
      "NumberRealEstateLoansOrLines            150000 non-null int64\n",
      "NumberOfTime60-89DaysPastDueNotWorse    150000 non-null int64\n",
      "NumberOfDependents                      146076 non-null float64\n",
      "dtypes: float64(4), int64(8)\n",
      "memory usage: 13.7 MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 101503 entries, 0 to 101502\n",
      "Data columns (total 12 columns):\n",
      "Unnamed: 0                              101503 non-null int64\n",
      "SeriousDlqin2yrs                        0 non-null float64\n",
      "RevolvingUtilizationOfUnsecuredLines    101503 non-null float64\n",
      "age                                     101503 non-null int64\n",
      "NumberOfTime30-59DaysPastDueNotWorse    101503 non-null int64\n",
      "DebtRatio                               101503 non-null float64\n",
      "MonthlyIncome                           81400 non-null float64\n",
      "NumberOfOpenCreditLinesAndLoans         101503 non-null int64\n",
      "NumberOfTimes90DaysLate                 101503 non-null int64\n",
      "NumberRealEstateLoansOrLines            101503 non-null int64\n",
      "NumberOfTime60-89DaysPastDueNotWorse    101503 non-null int64\n",
      "NumberOfDependents                      98877 non-null float64\n",
      "dtypes: float64(5), int64(7)\n",
      "memory usage: 9.3 MB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>1.202690e+05</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>146076.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>75000.500000</td>\n",
       "      <td>0.066840</td>\n",
       "      <td>6.048438</td>\n",
       "      <td>52.295207</td>\n",
       "      <td>0.421033</td>\n",
       "      <td>353.005076</td>\n",
       "      <td>6.670221e+03</td>\n",
       "      <td>8.452760</td>\n",
       "      <td>0.265973</td>\n",
       "      <td>1.018240</td>\n",
       "      <td>0.240387</td>\n",
       "      <td>0.757222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>43301.414527</td>\n",
       "      <td>0.249746</td>\n",
       "      <td>249.755371</td>\n",
       "      <td>14.771866</td>\n",
       "      <td>4.192781</td>\n",
       "      <td>2037.818523</td>\n",
       "      <td>1.438467e+04</td>\n",
       "      <td>5.145951</td>\n",
       "      <td>4.169304</td>\n",
       "      <td>1.129771</td>\n",
       "      <td>4.155179</td>\n",
       "      <td>1.115086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>37500.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029867</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.175074</td>\n",
       "      <td>3.400000e+03</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>75000.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.154181</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.366508</td>\n",
       "      <td>5.400000e+03</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>112500.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.559046</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.868254</td>\n",
       "      <td>8.249000e+03</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50708.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>329664.000000</td>\n",
       "      <td>3.008750e+06</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Unnamed: 0  SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  \\\n",
       "count  150000.000000     150000.000000                         150000.000000   \n",
       "mean    75000.500000          0.066840                              6.048438   \n",
       "std     43301.414527          0.249746                            249.755371   \n",
       "min         1.000000          0.000000                              0.000000   \n",
       "25%     37500.750000          0.000000                              0.029867   \n",
       "50%     75000.500000          0.000000                              0.154181   \n",
       "75%    112500.250000          0.000000                              0.559046   \n",
       "max    150000.000000          1.000000                          50708.000000   \n",
       "\n",
       "                 age  NumberOfTime30-59DaysPastDueNotWorse      DebtRatio  \\\n",
       "count  150000.000000                         150000.000000  150000.000000   \n",
       "mean       52.295207                              0.421033     353.005076   \n",
       "std        14.771866                              4.192781    2037.818523   \n",
       "min         0.000000                              0.000000       0.000000   \n",
       "25%        41.000000                              0.000000       0.175074   \n",
       "50%        52.000000                              0.000000       0.366508   \n",
       "75%        63.000000                              0.000000       0.868254   \n",
       "max       109.000000                             98.000000  329664.000000   \n",
       "\n",
       "       MonthlyIncome  NumberOfOpenCreditLinesAndLoans  \\\n",
       "count   1.202690e+05                    150000.000000   \n",
       "mean    6.670221e+03                         8.452760   \n",
       "std     1.438467e+04                         5.145951   \n",
       "min     0.000000e+00                         0.000000   \n",
       "25%     3.400000e+03                         5.000000   \n",
       "50%     5.400000e+03                         8.000000   \n",
       "75%     8.249000e+03                        11.000000   \n",
       "max     3.008750e+06                        58.000000   \n",
       "\n",
       "       NumberOfTimes90DaysLate  NumberRealEstateLoansOrLines  \\\n",
       "count            150000.000000                 150000.000000   \n",
       "mean                  0.265973                      1.018240   \n",
       "std                   4.169304                      1.129771   \n",
       "min                   0.000000                      0.000000   \n",
       "25%                   0.000000                      0.000000   \n",
       "50%                   0.000000                      1.000000   \n",
       "75%                   0.000000                      2.000000   \n",
       "max                  98.000000                     54.000000   \n",
       "\n",
       "       NumberOfTime60-89DaysPastDueNotWorse  NumberOfDependents  \n",
       "count                         150000.000000       146076.000000  \n",
       "mean                               0.240387            0.757222  \n",
       "std                                4.155179            1.115086  \n",
       "min                                0.000000            0.000000  \n",
       "25%                                0.000000            0.000000  \n",
       "50%                                0.000000            0.000000  \n",
       "75%                                0.000000            1.000000  \n",
       "max                               98.000000           20.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>8.140000e+04</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>98877.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>50752.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.310000</td>\n",
       "      <td>52.405436</td>\n",
       "      <td>0.453770</td>\n",
       "      <td>344.475020</td>\n",
       "      <td>6.855036e+03</td>\n",
       "      <td>8.453514</td>\n",
       "      <td>0.296691</td>\n",
       "      <td>1.013074</td>\n",
       "      <td>0.270317</td>\n",
       "      <td>0.769046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>29301.536524</td>\n",
       "      <td>NaN</td>\n",
       "      <td>196.156039</td>\n",
       "      <td>14.779756</td>\n",
       "      <td>4.538487</td>\n",
       "      <td>1632.595231</td>\n",
       "      <td>3.650860e+04</td>\n",
       "      <td>5.144100</td>\n",
       "      <td>4.515859</td>\n",
       "      <td>1.110253</td>\n",
       "      <td>4.503578</td>\n",
       "      <td>1.136778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>25376.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.030131</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.173423</td>\n",
       "      <td>3.408000e+03</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>50752.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.152586</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.364260</td>\n",
       "      <td>5.400000e+03</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>76127.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.564225</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.851619</td>\n",
       "      <td>8.200000e+03</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21821.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>268326.000000</td>\n",
       "      <td>7.727000e+06</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>43.000000</td>\n",
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      "text/plain": [
       "          Unnamed: 0  SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  \\\n",
       "count  101503.000000               0.0                         101503.000000   \n",
       "mean    50752.000000               NaN                              5.310000   \n",
       "std     29301.536524               NaN                            196.156039   \n",
       "min         1.000000               NaN                              0.000000   \n",
       "25%     25376.500000               NaN                              0.030131   \n",
       "50%     50752.000000               NaN                              0.152586   \n",
       "75%     76127.500000               NaN                              0.564225   \n",
       "max    101503.000000               NaN                          21821.000000   \n",
       "\n",
       "                 age  NumberOfTime30-59DaysPastDueNotWorse      DebtRatio  \\\n",
       "count  101503.000000                         101503.000000  101503.000000   \n",
       "mean       52.405436                              0.453770     344.475020   \n",
       "std        14.779756                              4.538487    1632.595231   \n",
       "min        21.000000                              0.000000       0.000000   \n",
       "25%        41.000000                              0.000000       0.173423   \n",
       "50%        52.000000                              0.000000       0.364260   \n",
       "75%        63.000000                              0.000000       0.851619   \n",
       "max       104.000000                             98.000000  268326.000000   \n",
       "\n",
       "       MonthlyIncome  NumberOfOpenCreditLinesAndLoans  \\\n",
       "count   8.140000e+04                    101503.000000   \n",
       "mean    6.855036e+03                         8.453514   \n",
       "std     3.650860e+04                         5.144100   \n",
       "min     0.000000e+00                         0.000000   \n",
       "25%     3.408000e+03                         5.000000   \n",
       "50%     5.400000e+03                         8.000000   \n",
       "75%     8.200000e+03                        11.000000   \n",
       "max     7.727000e+06                        85.000000   \n",
       "\n",
       "       NumberOfTimes90DaysLate  NumberRealEstateLoansOrLines  \\\n",
       "count            101503.000000                 101503.000000   \n",
       "mean                  0.296691                      1.013074   \n",
       "std                   4.515859                      1.110253   \n",
       "min                   0.000000                      0.000000   \n",
       "25%                   0.000000                      0.000000   \n",
       "50%                   0.000000                      1.000000   \n",
       "75%                   0.000000                      2.000000   \n",
       "max                  98.000000                     37.000000   \n",
       "\n",
       "       NumberOfTime60-89DaysPastDueNotWorse  NumberOfDependents  \n",
       "count                         101503.000000        98877.000000  \n",
       "mean                               0.270317            0.769046  \n",
       "std                                4.503578            1.136778  \n",
       "min                                0.000000            0.000000  \n",
       "25%                                0.000000            0.000000  \n",
       "50%                                0.000000            0.000000  \n",
       "75%                                0.000000            1.000000  \n",
       "max                               98.000000           43.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，训练集中年龄的最小值为0，而实际情况下借款人必须成年，这对于实际情况来说是不合理的，用平均值进行替换。而用于预测的数据集不存在这样的问题。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.loc[train['age'] < 18, 'age'] = train['age'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据清洗"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "重命名Unnamed列。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#列重命名\n",
    "train.rename(columns={'Unnamed: 0':'No'}, inplace=True)\n",
    "test.rename(columns={'Unnamed: 0':'No'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来进行空值填充等操作。首先查看每个特征下的空值情况。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No                                          0\n",
       "SeriousDlqin2yrs                            0\n",
       "RevolvingUtilizationOfUnsecuredLines        0\n",
       "age                                         0\n",
       "NumberOfTime30-59DaysPastDueNotWorse        0\n",
       "DebtRatio                                   0\n",
       "MonthlyIncome                           29731\n",
       "NumberOfOpenCreditLinesAndLoans             0\n",
       "NumberOfTimes90DaysLate                     0\n",
       "NumberRealEstateLoansOrLines                0\n",
       "NumberOfTime60-89DaysPastDueNotWorse        0\n",
       "NumberOfDependents                       3924\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_count = train.isnull().sum()\n",
    "missing_values_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No                                           0\n",
       "SeriousDlqin2yrs                        101503\n",
       "RevolvingUtilizationOfUnsecuredLines         0\n",
       "age                                          0\n",
       "NumberOfTime30-59DaysPastDueNotWorse         0\n",
       "DebtRatio                                    0\n",
       "MonthlyIncome                            20103\n",
       "NumberOfOpenCreditLinesAndLoans              0\n",
       "NumberOfTimes90DaysLate                      0\n",
       "NumberRealEstateLoansOrLines                 0\n",
       "NumberOfTime60-89DaysPastDueNotWorse         0\n",
       "NumberOfDependents                        2626\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_count = test.isnull().sum()\n",
    "missing_values_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "发现月收入和家庭成员数目特征都有着大量的空值。对月收入进行分析，对比退休和未退休人群的平均收入情况："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6664.136611272914, 6686.5059913187015)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "working_train = train.loc[(train['age'] >= 18) & (train['age'] <= 60)]\n",
    "retired_train = train.loc[train['age'] > 60]\n",
    "income_working = working_train['MonthlyIncome'].mean()\n",
    "income_retired = retired_train['MonthlyIncome'].mean()\n",
    "income_working, income_retired"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6628.752541914091, 7455.069893679063)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "working_test = test.loc[(test['age'] >= 18) & (test['age'] <= 60)]\n",
    "retired_test = test.loc[test['age'] > 60]\n",
    "income_working = working_test['MonthlyIncome'].mean()\n",
    "income_retired = retired_test['MonthlyIncome'].mean()\n",
    "income_working, income_retired"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "发现虽然测试集的平均收入相差不多，但是训练集相差较大。接下来查看中位数情况。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5416.0, 5223.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_working = working_train['MonthlyIncome'].median()\n",
    "income_retired = retired_train['MonthlyIncome'].median()\n",
    "income_working, income_retired"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5416.0, 5305.0)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_working = working_test['MonthlyIncome'].median()\n",
    "income_retired = retired_test['MonthlyIncome'].median()\n",
    "income_working, income_retired"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "两个数据的中位数相差都不大，用中位数进行填列。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['MonthlyIncome'].fillna(train['MonthlyIncome'].median(), inplace = True)\n",
    "test['MonthlyIncome'].fillna(test['MonthlyIncome'].median(), inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来查看家庭成员数目。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.0)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['NumberOfDependents'].median(), test['NumberOfDependents'].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用中位数填充空值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['NumberOfDependents'].fillna(train['NumberOfDependents'].median(), inplace = True)\n",
    "test['NumberOfDependents'].fillna(test['NumberOfDependents'].median(), inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据集分析\n",
    "编写一个数据可视化函数，能将某个特征与数据集的关系进行呈现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bin_plot(data, num_bin, string, a, b):\n",
    "    kwargs = dict(histtype='stepfilled', color = 'salmon', alpha=0.3, normed=True, bins=num_bin)\n",
    "    kwargs2 = dict(histtype='stepfilled', color = 'lightskyblue', alpha=0.5, normed=True, bins=num_bin)\n",
    "    plt.hist(data[data['SeriousDlqin2yrs'] == 0][string], label=\"no\", **kwargs2)\n",
    "    plt.hist(data[data['SeriousDlqin2yrs'] == 1][string], label=\"yes\", **kwargs)\n",
    "    plt.xlim(a, b)\n",
    "    plt.legend()\n",
    "    plt.xlabel(string)\n",
    "    plt.ylabel('ratio')\n",
    "    plt.title(string + ' - yes/no')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "展现年龄特征与标签的关系。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anac\\lib\\site-packages\\ipykernel_launcher.py:4: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  after removing the cwd from sys.path.\n",
      "D:\\anac\\lib\\site-packages\\ipykernel_launcher.py:5: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_plot(train, 40, 'age', 18, 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "展现月收入特征与标签的关系。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anac\\lib\\site-packages\\ipykernel_launcher.py:4: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  after removing the cwd from sys.path.\n",
      "D:\\anac\\lib\\site-packages\\ipykernel_launcher.py:5: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_plot(train, 1000, 'MonthlyIncome', 0, 30000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "先将除自己之外的家庭成员个数特征进行分箱，再绘制饼状图体限其与标签的关系。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_d = train\n",
    "train_d['NumberOfDependents'].fillna(train_d['NumberOfDependents'].median(), inplace = True)\n",
    "bins = [-1, 0, 1, 2, 5, 9, 44]\n",
    "group_names = ['0','1', '2', '3-5', '6-9', '10-44']\n",
    "dependent = pd.cut(train_d['NumberOfDependents'], bins, labels = group_names)\n",
    "train_d['NumberOfDependents'] = dependent\n",
    "def plot_dependents(data):\n",
    "    dep = data[data['SeriousDlqin2yrs'] == 1].groupby('NumberOfDependents').count()['SeriousDlqin2yrs'].index\n",
    "    dep_count_yes = data[data['SeriousDlqin2yrs'] == 1].groupby('NumberOfDependents').count()['SeriousDlqin2yrs']\n",
    "    dep_count_no = data[data['SeriousDlqin2yrs'] == 0].groupby('NumberOfDependents').count()['SeriousDlqin2yrs']\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    plt.subplot(121)\n",
    "    plt.title('yes')\n",
    "    plt.pie(dep_count_yes.values, labels=dep)\n",
    "    plt.subplot(122)\n",
    "    plt.title('no')\n",
    "    plt.pie(dep_count_no.values, labels=dep)\n",
    "    plt.show()\n",
    "plot_dependents(train_d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可视化特征与特征之间的关系。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SeriousDlqin2yrs', 'RevolvingUtilizationOfUnsecuredLines', 'age',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfOpenCreditLinesAndLoans', 'NumberOfTimes90DaysLate',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberOfDependents'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#取除了No之外的所有标签\n",
    "features = train.dtypes[train.dtypes != \"object\"].index\n",
    "features = features.drop('No')\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1733e6bed08>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制热力图查看特征与特征之间的相关性\n",
    "corr = train[features].corr()\n",
    "plt.figure(figsize = (18, 15))\n",
    "pic_sns = sns.heatmap(corr, annot=True, fmt='.2g', cmap = 'GnBu')\n",
    "bottom, top = pic_sns.get_ylim()\n",
    "pic_sns.set_ylim(bottom + 0.5, top - 0.5)\n",
    "pic_sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "观察发现，三个逾期还款的特征之间相关度都很高，且接近1，有可能出现了极端情况。查看这三个特征的箱型图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (18, 10))\n",
    "train[['NumberOfTime30-59DaysPastDueNotWorse',\n",
    "       'NumberOfTimes90DaysLate',\n",
    "       'NumberOfTime60-89DaysPastDueNotWorse']].boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练集中偏离合理数据数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1733</td>\n",
       "      <td>1734</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2286</td>\n",
       "      <td>2287</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3884</td>\n",
       "      <td>3885</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>98</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4417</td>\n",
       "      <td>4418</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4705</td>\n",
       "      <td>4706</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147774</td>\n",
       "      <td>147775</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>98</td>\n",
       "      <td>255.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149153</td>\n",
       "      <td>149154</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>98</td>\n",
       "      <td>54.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149239</td>\n",
       "      <td>149240</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149439</td>\n",
       "      <td>149440</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>98</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149769</td>\n",
       "      <td>149770</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>269 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            No  SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines   age  \\\n",
       "1733      1734                 1                                   1.0  27.0   \n",
       "2286      2287                 0                                   1.0  22.0   \n",
       "3884      3885                 0                                   1.0  38.0   \n",
       "4417      4418                 0                                   1.0  21.0   \n",
       "4705      4706                 0                                   1.0  21.0   \n",
       "...        ...               ...                                   ...   ...   \n",
       "147774  147775                 1                                   1.0  68.0   \n",
       "149153  149154                 1                                   1.0  24.0   \n",
       "149239  149240                 0                                   1.0  26.0   \n",
       "149439  149440                 1                                   1.0  34.0   \n",
       "149769  149770                 0                                   1.0  23.0   \n",
       "\n",
       "        NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "1733                                      98        0.0         2700.0   \n",
       "2286                                      98        0.0         5400.0   \n",
       "3884                                      98       12.0         5400.0   \n",
       "4417                                      98        0.0            0.0   \n",
       "4705                                      98        0.0         2000.0   \n",
       "...                                      ...        ...            ...   \n",
       "147774                                    98      255.0         5400.0   \n",
       "149153                                    98       54.0         5400.0   \n",
       "149239                                    98        0.0         2000.0   \n",
       "149439                                    98        9.0         5400.0   \n",
       "149769                                    98        0.0         5400.0   \n",
       "\n",
       "        NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "1733                                  0                       98   \n",
       "2286                                  0                       98   \n",
       "3884                                  0                       98   \n",
       "4417                                  0                       98   \n",
       "4705                                  0                       98   \n",
       "...                                 ...                      ...   \n",
       "147774                                0                       98   \n",
       "149153                                0                       98   \n",
       "149239                                0                       98   \n",
       "149439                                0                       98   \n",
       "149769                                0                       98   \n",
       "\n",
       "        NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "1733                               0                                    98   \n",
       "2286                               0                                    98   \n",
       "3884                               0                                    98   \n",
       "4417                               0                                    98   \n",
       "4705                               0                                    98   \n",
       "...                              ...                                   ...   \n",
       "147774                             0                                    98   \n",
       "149153                             0                                    98   \n",
       "149239                             0                                    98   \n",
       "149439                             0                                    98   \n",
       "149769                             0                                    98   \n",
       "\n",
       "        NumberOfDependents  \n",
       "1733                   0.0  \n",
       "2286                   0.0  \n",
       "3884                   0.0  \n",
       "4417                   0.0  \n",
       "4705                   0.0  \n",
       "...                    ...  \n",
       "147774                 0.0  \n",
       "149153                 0.0  \n",
       "149239                 0.0  \n",
       "149439                 0.0  \n",
       "149769                 0.0  \n",
       "\n",
       "[269 rows x 12 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.loc[train['NumberOfTime30-59DaysPastDueNotWorse'] > 90]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (18, 10))\n",
    "test[['NumberOfTime30-59DaysPastDueNotWorse',\n",
    "       'NumberOfTimes90DaysLate',\n",
    "       'NumberOfTime60-89DaysPastDueNotWorse']].boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23</td>\n",
       "      <td>98</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>98</td>\n",
       "      <td>0.020303</td>\n",
       "      <td>2708.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>307</td>\n",
       "      <td>308</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22</td>\n",
       "      <td>98</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>703</td>\n",
       "      <td>704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23</td>\n",
       "      <td>98</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1131</td>\n",
       "      <td>1132</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24</td>\n",
       "      <td>98</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99735</td>\n",
       "      <td>99736</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21</td>\n",
       "      <td>98</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100061</td>\n",
       "      <td>100062</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31</td>\n",
       "      <td>98</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2570.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100653</td>\n",
       "      <td>100654</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22</td>\n",
       "      <td>98</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101207</td>\n",
       "      <td>101208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42</td>\n",
       "      <td>98</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101482</td>\n",
       "      <td>101483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33</td>\n",
       "      <td>98</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>214 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            No  SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "9           10               NaN                                   1.0   23   \n",
       "69          70               NaN                                   1.0   52   \n",
       "307        308               NaN                                   1.0   22   \n",
       "703        704               NaN                                   1.0   23   \n",
       "1131      1132               NaN                                   1.0   24   \n",
       "...        ...               ...                                   ...  ...   \n",
       "99735    99736               NaN                                   1.0   21   \n",
       "100061  100062               NaN                                   1.0   31   \n",
       "100653  100654               NaN                                   1.0   22   \n",
       "101207  101208               NaN                                   1.0   42   \n",
       "101482  101483               NaN                                   1.0   33   \n",
       "\n",
       "        NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "9                                         98   0.000000            0.0   \n",
       "69                                        98   0.020303         2708.0   \n",
       "307                                       98   0.000000            0.0   \n",
       "703                                       98   0.000000         5400.0   \n",
       "1131                                      98   0.000000         5400.0   \n",
       "...                                      ...        ...            ...   \n",
       "99735                                     98   0.000000         5400.0   \n",
       "100061                                    98   0.000000         2570.0   \n",
       "100653                                    98   0.000000         5400.0   \n",
       "101207                                    98  12.000000         5400.0   \n",
       "101482                                    98   0.000000         2000.0   \n",
       "\n",
       "        NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "9                                     0                       98   \n",
       "69                                    0                       98   \n",
       "307                                   0                       98   \n",
       "703                                   0                       98   \n",
       "1131                                  0                       98   \n",
       "...                                 ...                      ...   \n",
       "99735                                 0                       98   \n",
       "100061                                0                       98   \n",
       "100653                                0                       98   \n",
       "101207                                0                       98   \n",
       "101482                                0                       98   \n",
       "\n",
       "        NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "9                                  0                                    98   \n",
       "69                                 0                                    98   \n",
       "307                                0                                    98   \n",
       "703                                0                                    98   \n",
       "1131                               0                                    98   \n",
       "...                              ...                                   ...   \n",
       "99735                              0                                    98   \n",
       "100061                             0                                    98   \n",
       "100653                             0                                    98   \n",
       "101207                             0                                    98   \n",
       "101482                             0                                    98   \n",
       "\n",
       "        NumberOfDependents  \n",
       "9                      0.0  \n",
       "69                     0.0  \n",
       "307                    0.0  \n",
       "703                    0.0  \n",
       "1131                   0.0  \n",
       "...                    ...  \n",
       "99735                  0.0  \n",
       "100061                 1.0  \n",
       "100653                 0.0  \n",
       "101207                 0.0  \n",
       "101482                 3.0  \n",
       "\n",
       "[214 rows x 12 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.loc[test['NumberOfTime30-59DaysPastDueNotWorse'] > 90]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由箱型图可知，训练集和测试集中都出现了不合理的、与其他数据偏差很大的少数数据，对最终的结果造成了较大的影响。对这些非常规数据进行替换，替换为中位数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_abnormal(data, feats):\n",
    "    for f in feats:\n",
    "        median = data[f].median()\n",
    "        for i in range(len(data[f])):\n",
    "            if data[f][i] > 90:\n",
    "                data[f][i] = median\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anac\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "train = replace_abnormal(train, ['NumberOfTime30-59DaysPastDueNotWorse','NumberOfTimes90DaysLate','NumberOfTime60-89DaysPastDueNotWorse'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (18, 10))\n",
    "train[['NumberOfTime30-59DaysPastDueNotWorse',\n",
    "       'NumberOfTimes90DaysLate',\n",
    "       'NumberOfTime60-89DaysPastDueNotWorse']].boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anac\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "test = replace_abnormal(test, ['NumberOfTime30-59DaysPastDueNotWorse','NumberOfTimes90DaysLate','NumberOfTime60-89DaysPastDueNotWorse'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c0661d0748>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制热力图查看特征与特征之间的相关性\n",
    "corr = train[features].corr()\n",
    "plt.figure(figsize=(18, 15))\n",
    "sns.heatmap(corr, annot=True, fmt='.2g', cmap = 'GnBu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 构建模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先将标签和数据进行分离。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop(['No', 'SeriousDlqin2yrs'], axis = 1)\n",
    "y = train['SeriousDlqin2yrs']\n",
    "pred = test.drop(['No', 'SeriousDlqin2yrs'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将数据标准化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler().fit(X)\n",
    "X_scaled = scaler.transform(X)\n",
    "scaler2 = StandardScaler().fit(pred)\n",
    "pred_scaled = scaler.transform(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.02067197, -0.62925278, -0.35189773, ..., -0.90128301,\n",
       "        -0.19620723, -0.66612604],\n",
       "       [-0.02236253,  0.31849795, -0.35189773, ...,  2.63926936,\n",
       "        -0.19620723,  1.14052977],\n",
       "       [-0.02404426,  0.45389091, -0.35189773, ..., -0.01614492,\n",
       "        -0.19620723,  1.14052977],\n",
       "       ...,\n",
       "       [-0.02389082,  1.1985522 , -0.35189773, ..., -0.90128301,\n",
       "        -0.19620723, -0.66612604],\n",
       "       [-0.02287439,  0.25080147, -0.35189773, ...,  0.86899317,\n",
       "         2.83604952,  2.04385767],\n",
       "       [-0.02244843, -1.57700351, -0.35189773, ..., -0.90128301,\n",
       "        -0.19620723, -0.66612604]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_scaled = pd.DataFrame(pred_scaled, columns = pred.columns, index = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>101503.000000</td>\n",
       "      <td>101503.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>-0.002957</td>\n",
       "      <td>0.007462</td>\n",
       "      <td>0.002613</td>\n",
       "      <td>-0.004186</td>\n",
       "      <td>0.011513</td>\n",
       "      <td>0.000146</td>\n",
       "      <td>-0.000407</td>\n",
       "      <td>-0.004573</td>\n",
       "      <td>-0.002985</td>\n",
       "      <td>0.010602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.785395</td>\n",
       "      <td>1.000537</td>\n",
       "      <td>1.012213</td>\n",
       "      <td>0.801151</td>\n",
       "      <td>2.536711</td>\n",
       "      <td>0.999644</td>\n",
       "      <td>0.984917</td>\n",
       "      <td>0.982727</td>\n",
       "      <td>0.990101</td>\n",
       "      <td>1.019496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>-0.024218</td>\n",
       "      <td>-2.118575</td>\n",
       "      <td>-0.351898</td>\n",
       "      <td>-0.173228</td>\n",
       "      <td>-0.497927</td>\n",
       "      <td>-1.642610</td>\n",
       "      <td>-0.186131</td>\n",
       "      <td>-0.901283</td>\n",
       "      <td>-0.196207</td>\n",
       "      <td>-0.666126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>-0.024097</td>\n",
       "      <td>-0.764646</td>\n",
       "      <td>-0.351898</td>\n",
       "      <td>-0.173142</td>\n",
       "      <td>-0.195375</td>\n",
       "      <td>-0.670969</td>\n",
       "      <td>-0.186131</td>\n",
       "      <td>-0.901283</td>\n",
       "      <td>-0.196207</td>\n",
       "      <td>-0.666126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>-0.023607</td>\n",
       "      <td>-0.019984</td>\n",
       "      <td>-0.351898</td>\n",
       "      <td>-0.173049</td>\n",
       "      <td>-0.079009</td>\n",
       "      <td>-0.087984</td>\n",
       "      <td>-0.186131</td>\n",
       "      <td>-0.016145</td>\n",
       "      <td>-0.196207</td>\n",
       "      <td>-0.666126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>-0.021958</td>\n",
       "      <td>0.724677</td>\n",
       "      <td>-0.351898</td>\n",
       "      <td>-0.172810</td>\n",
       "      <td>0.074129</td>\n",
       "      <td>0.495001</td>\n",
       "      <td>-0.186131</td>\n",
       "      <td>0.868993</td>\n",
       "      <td>-0.196207</td>\n",
       "      <td>0.237202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>87.345566</td>\n",
       "      <td>3.500233</td>\n",
       "      <td>26.898830</td>\n",
       "      <td>131.500369</td>\n",
       "      <td>598.942620</td>\n",
       "      <td>14.875286</td>\n",
       "      <td>36.919218</td>\n",
       "      <td>31.848826</td>\n",
       "      <td>27.094104</td>\n",
       "      <td>38.176974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       RevolvingUtilizationOfUnsecuredLines            age  \\\n",
       "count                         101503.000000  101503.000000   \n",
       "mean                              -0.002957       0.007462   \n",
       "std                                0.785395       1.000537   \n",
       "min                               -0.024218      -2.118575   \n",
       "25%                               -0.024097      -0.764646   \n",
       "50%                               -0.023607      -0.019984   \n",
       "75%                               -0.021958       0.724677   \n",
       "max                               87.345566       3.500233   \n",
       "\n",
       "       NumberOfTime30-59DaysPastDueNotWorse      DebtRatio  MonthlyIncome  \\\n",
       "count                         101503.000000  101503.000000  101503.000000   \n",
       "mean                               0.002613      -0.004186       0.011513   \n",
       "std                                1.012213       0.801151       2.536711   \n",
       "min                               -0.351898      -0.173228      -0.497927   \n",
       "25%                               -0.351898      -0.173142      -0.195375   \n",
       "50%                               -0.351898      -0.173049      -0.079009   \n",
       "75%                               -0.351898      -0.172810       0.074129   \n",
       "max                               26.898830     131.500369     598.942620   \n",
       "\n",
       "       NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "count                    101503.000000            101503.000000   \n",
       "mean                          0.000146                -0.000407   \n",
       "std                           0.999644                 0.984917   \n",
       "min                          -1.642610                -0.186131   \n",
       "25%                          -0.670969                -0.186131   \n",
       "50%                          -0.087984                -0.186131   \n",
       "75%                           0.495001                -0.186131   \n",
       "max                          14.875286                36.919218   \n",
       "\n",
       "       NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "count                 101503.000000                         101503.000000   \n",
       "mean                      -0.004573                             -0.002985   \n",
       "std                        0.982727                              0.990101   \n",
       "min                       -0.901283                             -0.196207   \n",
       "25%                       -0.901283                             -0.196207   \n",
       "50%                       -0.016145                             -0.196207   \n",
       "75%                        0.868993                             -0.196207   \n",
       "max                       31.848826                             27.094104   \n",
       "\n",
       "       NumberOfDependents  \n",
       "count       101503.000000  \n",
       "mean             0.010602  \n",
       "std              1.019496  \n",
       "min             -0.666126  \n",
       "25%             -0.666126  \n",
       "50%             -0.666126  \n",
       "75%              0.237202  \n",
       "max             38.176974  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_scaled.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_scaled = pd.DataFrame(X_scaled, columns = X.columns, index = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-0.021150</td>\n",
       "      <td>-0.493860</td>\n",
       "      <td>2.516600</td>\n",
       "      <td>-0.172833</td>\n",
       "      <td>0.209579</td>\n",
       "      <td>0.883657</td>\n",
       "      <td>-0.186131</td>\n",
       "      <td>4.409546</td>\n",
       "      <td>-0.196207</td>\n",
       "      <td>1.140530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.020385</td>\n",
       "      <td>-0.832342</td>\n",
       "      <td>-0.351898</td>\n",
       "      <td>-0.173168</td>\n",
       "      <td>-0.296226</td>\n",
       "      <td>-0.865297</td>\n",
       "      <td>-0.186131</td>\n",
       "      <td>-0.901283</td>\n",
       "      <td>-0.196207</td>\n",
       "      <td>0.237202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.021582</td>\n",
       "      <td>-0.967735</td>\n",
       "      <td>1.082351</td>\n",
       "      <td>-0.173186</td>\n",
       "      <td>-0.261937</td>\n",
       "      <td>-1.253953</td>\n",
       "      <td>1.875277</td>\n",
       "      <td>-0.901283</td>\n",
       "      <td>-0.196207</td>\n",
       "      <td>-0.666126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.023281</td>\n",
       "      <td>-1.509307</td>\n",
       "      <td>-0.351898</td>\n",
       "      <td>-0.173210</td>\n",
       "      <td>-0.241922</td>\n",
       "      <td>-0.670969</td>\n",
       "      <td>-0.186131</td>\n",
       "      <td>-0.901283</td>\n",
       "      <td>-0.196207</td>\n",
       "      <td>-0.666126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.020585</td>\n",
       "      <td>-0.223074</td>\n",
       "      <td>1.082351</td>\n",
       "      <td>-0.173215</td>\n",
       "      <td>4.435064</td>\n",
       "      <td>-0.282312</td>\n",
       "      <td>-0.186131</td>\n",
       "      <td>-0.016145</td>\n",
       "      <td>-0.196207</td>\n",
       "      <td>-0.666126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RevolvingUtilizationOfUnsecuredLines       age  \\\n",
       "0                             -0.021150 -0.493860   \n",
       "1                             -0.020385 -0.832342   \n",
       "2                             -0.021582 -0.967735   \n",
       "3                             -0.023281 -1.509307   \n",
       "4                             -0.020585 -0.223074   \n",
       "\n",
       "   NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "0                              2.516600  -0.172833       0.209579   \n",
       "1                             -0.351898  -0.173168      -0.296226   \n",
       "2                              1.082351  -0.173186      -0.261937   \n",
       "3                             -0.351898  -0.173210      -0.241922   \n",
       "4                              1.082351  -0.173215       4.435064   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                         0.883657                -0.186131   \n",
       "1                        -0.865297                -0.186131   \n",
       "2                        -1.253953                 1.875277   \n",
       "3                        -0.670969                -0.186131   \n",
       "4                        -0.282312                -0.186131   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "0                      4.409546                             -0.196207   \n",
       "1                     -0.901283                             -0.196207   \n",
       "2                     -0.901283                             -0.196207   \n",
       "3                     -0.901283                             -0.196207   \n",
       "4                     -0.016145                             -0.196207   \n",
       "\n",
       "   NumberOfDependents  \n",
       "0            1.140530  \n",
       "1            0.237202  \n",
       "2           -0.666126  \n",
       "3           -0.666126  \n",
       "4           -0.666126  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "划分数据集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "#以4：1的比例划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, random_state=10, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建梯度提升决策树模型(lightgbm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb\n",
    "from sklearn.metrics import roc_curve, roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "class gbm_model():\n",
    "    def __init__(self, X_train, X_test, y_train, y_test, pred):\n",
    "        self.X_train = X_train.astype(float)\n",
    "        self.X_test = X_test.astype(float)\n",
    "        self.y_train = y_train.astype(float)\n",
    "        self.y_test = y_test.astype(float)\n",
    "        self.pred = pred.astype(float)\n",
    "        self.feature_name = [i for i in self.X_train.columns]\n",
    "        self.lgb_train = lgb.Dataset(data=self.X_train[self.feature_name],\n",
    "                                     label=self.y_train,\n",
    "                                     feature_name=self.feature_name)\n",
    "        self.lgb_test = lgb.Dataset(data=self.X_test[self.feature_name],\n",
    "                                    label=self.y_test,\n",
    "                                    feature_name=self.feature_name)\n",
    "        self.param = self.basic()\n",
    "        self.gbm = None\n",
    "        self.evals_result = {}\n",
    "    \n",
    "    def basic(self):\n",
    "        param = dict()\n",
    "        param['objective'] = 'binary'\n",
    "        param['boosting_type'] = 'gbdt'\n",
    "        param['metric'] = 'auc'\n",
    "        param['verbose'] = 0\n",
    "        param['learning_rate'] = 0.1\n",
    "        param['max_depth'] = -1\n",
    "        param['feature_fraction'] = 0.8\n",
    "        param['bagging_fraction'] = 0.8\n",
    "        param['bagging_freq'] = 1\n",
    "        param['num_leaves'] = 15\n",
    "        param['min_data_in_leaf'] = 64\n",
    "        param['is_unbalance'] = False\n",
    "        param['verbose'] = -1\n",
    "        return param\n",
    "    \n",
    "    def fit(self):\n",
    "        self.gbm = lgb.train(self.param,\n",
    "                             self.lgb_train,\n",
    "                             early_stopping_rounds=10,\n",
    "                             num_boost_round=1000,\n",
    "                             evals_result=self.evals_result,\n",
    "                             valid_sets=[self.lgb_train, self.lgb_test],\n",
    "                             verbose_eval=10)\n",
    "        \n",
    "    def evaluate_train(self):\n",
    "        prob_label = self.gbm.predict(self.X_train[self.feature_name])\n",
    "        auc = roc_auc_score(y_train, prob_label)\n",
    "        return auc\n",
    "    \n",
    "    def evaluate_test(self):\n",
    "        prob_label = self.gbm.predict(self.X_test[self.feature_name])\n",
    "        auc = roc_auc_score(y_test, prob_label)\n",
    "        return auc\n",
    "    \n",
    "    def save_predict(self):\n",
    "        prob_label = self.gbm.predict(self.pred[self.feature_name])\n",
    "        ids = [i+1 for i in range(self.pred.shape[0])]\n",
    "        ids = np.asarray(ids)\n",
    "        file = pd.DataFrame({'Id': ids, 'Probability': prob_label})\n",
    "        file.to_csv('data' + r'\\result_gbm.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RevolvingUtilizationOfUnsecuredLines', 'age',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfOpenCreditLinesAndLoans', 'NumberOfTimes90DaysLate',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberOfDependents'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = X_train.dtypes[X_train.dtypes != \"object\"].index\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbm = gbm_model(X_train, X_test, y_train, y_test, pred_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 10 rounds\n",
      "[10]\ttraining's auc: 0.858871\tvalid_1's auc: 0.863194\n",
      "[20]\ttraining's auc: 0.86289\tvalid_1's auc: 0.866042\n",
      "[30]\ttraining's auc: 0.865979\tvalid_1's auc: 0.868583\n",
      "[40]\ttraining's auc: 0.868147\tvalid_1's auc: 0.869322\n",
      "[50]\ttraining's auc: 0.86995\tvalid_1's auc: 0.870167\n",
      "[60]\ttraining's auc: 0.871797\tvalid_1's auc: 0.870338\n",
      "[70]\ttraining's auc: 0.873146\tvalid_1's auc: 0.870429\n",
      "[80]\ttraining's auc: 0.874341\tvalid_1's auc: 0.870632\n",
      "[90]\ttraining's auc: 0.875648\tvalid_1's auc: 0.870495\n",
      "Early stopping, best iteration is:\n",
      "[81]\ttraining's auc: 0.874442\tvalid_1's auc: 0.870726\n"
     ]
    }
   ],
   "source": [
    "gbm.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbm_auc_train = gbm.evaluate_train()\n",
    "gbm_auc_test = gbm.evaluate_test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8744422242276151, 0.8707261904232492)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbm_auc_train, gbm_auc_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbm.save_predict()"
   ]
  }
 ],
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